Other formats:
BibTeX
LaTeX
RIS
@article{2417777,
author = {Mitu, Bilkis and Trojan, Václav and Halámková, Lenka},
article_location = {UNITED STATES},
article_number = {23},
doi = {http://dx.doi.org/10.3390/s23239412},
keywords = {ATR FT-IR spectroscopy; machine learning; artificial neural network (ANN); partial least-square discriminant analysis (PLS-DA); male; female},
language = {eng},
issn = {2379-3694},
journal = {ACS SENSORS},
title = {Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context},
url = {https://www.mdpi.com/1424-8220/23/23/9412},
volume = {23},
year = {2023}
}
TY - JOUR
ID - 2417777
AU - Mitu, Bilkis - Trojan, Václav - Halámková, Lenka
PY - 2023
TI - Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context
JF - ACS SENSORS
VL - 23
IS - 23
SP - Neuvedeno
EP - Neuvedeno
PB - AMER CHEMICAL SOC
SN - 23793694
KW - ATR FT-IR spectroscopy
KW - machine learning
KW - artificial neural network (ANN)
KW - partial least-square discriminant analysis (PLS-DA)
KW - male
KW - female
UR - https://www.mdpi.com/1424-8220/23/23/9412
N2 - This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.
ER -
MITU, Bilkis, Václav TROJAN and Lenka HALÁMKOVÁ. Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context. \textit{ACS SENSORS}. UNITED STATES: AMER CHEMICAL SOC, 2023, vol.~23, No~23, p.~Neuvedeno, 15 pp. ISSN~2379-3694. Available from: https://dx.doi.org/10.3390/s23239412.
|
